Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.

Ogbomo-Harmitt, Shaheim; Muffoletto, Marica; Zeidan, Aya; Qureshi, Ahmed; King, Andrew P; Aslanidi, Oleg

Ogbomo-Harmitt, Shaheim; Muffoletto, Marica; Zeidan, Aya; Qureshi, Ahmed; King, Andrew P; Aslanidi, Oleg.

Afiliação

Ogbomo-Harmitt S; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Muffoletto M; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Zeidan A; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Qureshi A; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
King AP; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Aslanidi O; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.

Front Physiol ; 14: 1054401, 2023.

Article em En | MEDLINE | ID: mdl-36998987

Palavras-chave

atrial fibrillation; cardiac modelling; catheter ablation; deep learning; interpretability; medical imaging

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Physiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

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